IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0228885.html
   My bibliography  Save this article

Important citation identification by exploiting content and section-wise in-text citation count

Author

Listed:
  • Shahzad Nazir
  • Muhammad Asif
  • Shahbaz Ahmad
  • Faisal Bukhari
  • Muhammad Tanvir Afzal
  • Hanan Aljuaid

Abstract

A citation is deemed as a potential parameter to determine linkage between research articles. The parameter has extensively been employed to form multifarious academic aspects like calculating the impact factor of journals, h-Index of researchers, allocate different research grants, find the latest research trends, etc. The current state-of-the-art contends that all citations are not of equal importance. Based on this argument, the current trend in citation classification community categorizes citations into important and non-important reasons. The community has proposed different approaches to extract important citations such as citation count, context-based, metadata, and textual based approaches. The contemporary state-of-the-art in citation classification community ignores significantly potential features that can play a vital role in citation classification. This research presents a novel approach for binary citation classification by exploiting section-wise in-text citation frequencies, similarity score, and overall citation count-based features. The study also introduces machine learning algorithms based novel approach for assigning appropriate weights to the logical sections of research papers. The weights are allocated to the citations with respect to their sections. To perform the classification, we used three classification techniques, Support Vector Machine, Kernel Linear Regression, and Random Forest. The experiment was performed on two annotated benchmark datasets that contain 465 and 311 citation pairs of research articles respectively. The results revealed that the proposed approach attained an improved value of precision (i.e., 0.84 vs 0.72) from contemporary state-of-the-art approach.

Suggested Citation

  • Shahzad Nazir & Muhammad Asif & Shahbaz Ahmad & Faisal Bukhari & Muhammad Tanvir Afzal & Hanan Aljuaid, 2020. "Important citation identification by exploiting content and section-wise in-text citation count," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-19, March.
  • Handle: RePEc:plo:pone00:0228885
    DOI: 10.1371/journal.pone.0228885
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228885
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0228885&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0228885?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xiaodan Zhu & Peter Turney & Daniel Lemire & André Vellino, 2015. "Measuring academic influence: Not all citations are equal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 408-427, February.
    2. Fahri Karakaya & Abhrawashyu Awasthi, 2014. "Robustness and sensitivity of conjoint analysis versus multiple linear regression analysis," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 6(2), pages 121-136.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Faiza Qayyum & Harun Jamil & Naeem Iqbal & DoHyeun Kim & Muhammad Tanvir Afzal, 2022. "Toward potential hybrid features evaluation using MLP-ANN binary classification model to tackle meaningful citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6471-6499, November.
    2. Indra Budi & Yaniasih Yaniasih, 2023. "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 735-759, January.
    3. Setio Basuki & Masatoshi Tsuchiya, 2022. "SDCF: semi-automatically structured dataset of citation functions," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4569-4608, August.
    4. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Faiza Qayyum & Harun Jamil & Naeem Iqbal & DoHyeun Kim & Muhammad Tanvir Afzal, 2022. "Toward potential hybrid features evaluation using MLP-ANN binary classification model to tackle meaningful citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6471-6499, November.
    2. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    3. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.
    4. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
    5. Hamid R. Jamali & Majid Nabavi & Saeid Asadi, 2018. "How video articles are cited, the case of JoVE: Journal of Visualized Experiments," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1821-1839, December.
    6. Xin An & Xin Sun & Shuo Xu, 2022. "Important citations identification with semi-supervised classification model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6533-6555, November.
    7. CholMyong Pak & Guang Yu & Weibin Wang, 2018. "A study on the citation situation within the citing paper: citation distribution of references according to mention frequency," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 905-918, March.
    8. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    9. Błoński Krzysztof, 2023. "Analysis of Citations and Co-Citations of the Term ‘Word of Mouth’ Based on Publications in the Field of Social Sciences," Marketing of Scientific and Research Organizations, Sciendo, vol. 48(2), pages 111-133, June.
    10. Sarma, Gopal P., 2017. "Scientific Literature Text Mining and the Case for Open Access," OSF Preprints n6zqn_v1, Center for Open Science.
    11. Zhaoping Yan & Kaiyu Fan, 2024. "An integrated indicator for evaluating scientific papers: considering academic impact and novelty," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6909-6929, November.
    12. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    13. Weibin Wang & Zheng Wang & Tian Yu & CholMyong Pak & Guang Yu, 2020. "Research on citation mention times and contributions using a neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2383-2400, December.
    14. Hou, Jianhua & Tang, Shiqi & Zhang, Yang & Song, Haoyang, 2023. "Does prior knowledge affect patent technology diffusion? A semantic-based patent citation contribution analysis," Journal of Informetrics, Elsevier, vol. 17(2).
    15. Fang Zhang & Shengli Wu, 2021. "Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7197-7222, August.
    16. Alexander Serenko & Mauricio Marrone & John Dumay, 2022. "Scientometric portraits of recognized scientists: a structured literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4827-4846, August.
    17. Christin Katharina Kreutz & Premtim Sahitaj & Ralf Schenkel, 2020. "Evaluating semantometrics from computer science publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2915-2954, December.
    18. Taşkın, Zehra & Doğan, Güleda & Kulczycki, Emanuel & Zuccala, Alesia Ann, 2021. "Self-Citation Patterns of Journals Indexed in the Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 15(4).
    19. Pak, Chol Myong & Wang, Weibin & Yu, Guang, 2020. "An analysis of in-text citations based on fractional counting," Journal of Informetrics, Elsevier, vol. 14(4).
    20. Ruijie Wang & Yuhao Zhou & An Zeng, 2023. "Evaluating scientists by citation and disruption of their representative works," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1689-1710, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0228885. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.